首页> 外文期刊>Journal of Petroleum Science & Engineering >Estimation of shear wave velocity from post-stack seismic data through committee machine with cuckoo search optimized intelligence models
【24h】

Estimation of shear wave velocity from post-stack seismic data through committee machine with cuckoo search optimized intelligence models

机译:Cuckoo搜索优化智能模型委员会机器从堆栈后地震数据估算剪力波速度的估算

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Shear wave velocity (VS) afford petroleum engineers to favorable information for implementing of geomechanical, geophysical, and reservoir characterization studies. Hence, it is all-important to present a scheme for estimation of this parameter. The primary objective of this study is introducing a novel strategy for determining shear wave velocity from seismic data. For achieving the aforementioned objective, four-steps procedure are followed in this study: (1) Suitable seismic attributes (SAs) are selected as independent variables to estimate shear wave velocity by using step-wise regression method; (2) Input variables which selected in the first step are transformed into higher correlated data space through alternation conditional expectation (ACE); (3) Quantitative formulation between shear wave velocity and ACE transformed of input variables is made through three improved models namely optimized neural network (ONN), optimized support vector regression (OSVR), and optimized fuzzy inference system (OFIS). Optimization implementation of intelligence models are achieved through cuckoo search method (CS); (4) Committee machine (CM) using cuckoo search method is employed for integrating optimized models so reaps those benefits. The efficiency of proposed models is assessed founded on statistical parameters. The obtained results corroborate the superb performance of committee machine in preference to its elements. This paper infers the proposed strategy is suitable for modeling of shear wave velocity as a function of seismic data.
机译:剪切波速度(VS)为实现地质力学,地球物理和储层特征研究提供有利信息。因此,介绍估计此参数的方案是全面的。本研究的主要目的是引入一种用于从地震数据确定剪切波速度的新策略。为了实现上述目的,在本研究中遵循四步骤:(1)选择合适的地震属性(SAS)作为独立变量,通过使用步骤回归方法来估计剪切波速度; (2)在第一步中选择的输入变量通过交替的条件期望(ACE)转换为更高的相关数据空间; (3)通过三种改进的模型进行了剪切波速度和抗剪切波速度和ACE之间的定量制剂,即优化的神经网络(ONN),优化支持向量回归(OSVR)和优化的模糊推理系统(OFIS)。通过Cuckoo搜索方法(CS)实现智能模型的优化实现; (4)委员会机(CM)使用杜鹃搜索方法用于集成优化的型号,所以收获这些益处。评估拟议模型的效率在统计参数上创立。所获得的结果证实了委员会机器的优于其元素的卓越性能。本文揭示了所提出的策略适用于作为地震数据的函数的剪力波速度建模。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号